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cs.CR2024
A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
Mohamed elShehaby, Ashraf Matrawy
As network security threats evolve, safeguarding flow-based Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS) from evasion adversarial attacks is crucial. This…
cs.LG2024
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness
Mohamed elShehaby, Aditya Kotha, Ashraf Matrawy
Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in netwo…